Statistic-Based Method for Budgetary Control Limits Setting—Renewed Approach in the Context of Industry 4.0

  • Zdzisław Kes
  • Krzysztof NowosielskiEmail author
Part of the Studies in Computational Intelligence book series (SCI, volume 887)


The chapter presents the issue of budget variance analysis (BVA), as one of the most important processes in Management Control (MC). Based on the current state of knowledge, shortcomings in methods for setting the budgetary control limits (BCL) were indicated. In short, BCL enables focusing on a significant budget variances. In business practice, BCL setting is mostly based on intuition and individual judgment of managers rather than on numerical calculation using IT systems. In the era of Industry 4.0 this kind of solutions are far from being enough to make right decisions in the right way. Therefore, the main research objective of the presented work was to develop effective and applicable method for BLC setting, which works in an objective manner. We decided to renew the idea of Shewhart’s control charts implementation in the BCL setting. Design science research (DSR) method was used to reach the research objective, starting with problem definition, through developing a new method for BCL setting, ending with its test and evaluation.


Management control systems Budgeting Budgetary control limits Control charts Design science research 



The project is financed by the Ministry of Science and Higher Education in Poland under the programme “Regional Initiative of Excellence” 2019–2022 project number 015/RID/2018/19 total funding amount 10,721,040.00 PLN.


  1. 1.
    Anthony, R. N. (1965). Planning and control systems: A framework for analysis. Boston: Division of Research, Harvard Business School.Google Scholar
  2. 2.
    Anthony, R. N. (1973). Some fruitful directions for research in management accounting. In N. Dopuch, & L. Revsine (Eds.), Accounting research 1960–1970: A critical evaluation. Center for International Education and Research in Accounting, University of Illinois.Google Scholar
  3. 3.
    Archer, B. (1992). The nature of research in design and technology education. Loughborough: Loughborough University.Google Scholar
  4. 4.
    Armstrong, M. (2006). A handbook of management techniques. London: Kogan Page.Google Scholar
  5. 5.
    Bierman, H., Jr., Fouraker, L. E., & Jaddicke, R. K. (1961). A use of probability and statistics in performance evaluation. Accounting Review, 36, 7.Google Scholar
  6. 6.
    Blocher, E. J., Chen, K. H., & Lin, W. T. (1999). Cost management. A strategic emphasis. Boston: McGraw-Hill/Irwin.Google Scholar
  7. 7.
    Drury, C. (2012). Management and cost accounting. Hampshire: Cengage Learning.Google Scholar
  8. 8.
    Duncan, A. (1956). The economic design of X charts used to maintain current control of a process. Journal of the American Statistical Association, LI, 228–242.zbMATHGoogle Scholar
  9. 9.
    Duvall, R. M. (1967). Rules for investigating cost variances. Management Science, XIII, 631–641.Google Scholar
  10. 10.
    Eppler, M. J. (2006). Managing information quality. Berlin, Heidelberg: Springer.Google Scholar
  11. 11.
    Figueiredo, A. D., & Cunha, P. R. (2007). Action research and design in information systems: Two faces of a single coin. In N. Kock (Ed.), Information systems action research: An applied view of emerging concepts and methods (pp. 61–96). New York, NY: Springer.CrossRefGoogle Scholar
  12. 12.
    Girshick, M. A., & Rubin, H. (1952). A Bayes approach to a quality control model. Annals of Mathematical Statistics, XXIII, 114–125.Google Scholar
  13. 13.
    Goel, A. L., & Wu, S. M. (1973) Economically optimum design of cusum charts. Management Science, XIX, 1271–1282.Google Scholar
  14. 14.
    Günther, T. W. (2013). Conceptualisations of ‘controlling’ in German-speaking countries: Analysis and comparison with Anglo-American management control frameworks. Journal of Management Control, 23, 269–290.CrossRefGoogle Scholar
  15. 15.
    Hahn, D., & Hungenberg, H. (2001). PuK Planungs- und Kontrollrechnung (6th ed.). Wiesbaden: Gabler.Google Scholar
  16. 16.
    Hevner, A., March, S. T., & Park, J. (2004). Design science in information systems research. MIS Quarterly, 28(1), 75–105.CrossRefGoogle Scholar
  17. 17.
    Horváth, P. (1978). Controlling—Entwicklung und Stand einer Konzeption zur Lösung der Adaptions- und Koordinationsprobleme der Führung. Zeitschrift für Betriebswirtschaft, 48(3), 194–208.Google Scholar
  18. 18.
    Iivari, J. (2015). Distinguishing and contrasting two strategies for design science research. European Journal of Information Systems, 24, 107–115.CrossRefGoogle Scholar
  19. 19.
    Kaplan, R. S. (1975). The significance and investigation of cost variances: Survey and extensions. Journal of Accounting Research, 13(2), 311–337.CrossRefGoogle Scholar
  20. 20.
    Kes, Z. (1999). Control limits for deviations as part of the budget performance evaluation. Scientific Works of the Wrocław University of Economics, Wrocław, No. 831.Google Scholar
  21. 21.
    Koronacki, J., & Thompson, J. R. (1994). Statystyczne sterowanie procesem. PLJ Warszawa: Metoda Deminga etapowej optymalizacji jakości.Google Scholar
  22. 22.
    Kuźmiński, Ł., & Peternek, P. (2005). Using control charts to stabilize economic processes. Scientific Works of the University of Economics, No. 1096. Wrocław: Publisher AE.Google Scholar
  23. 23.
    Kwang, Ch-W, & Slavin, A. (1962). The simple mathematics of variance analysis. Accounting Review, 37, 7.Google Scholar
  24. 24.
    Malmi, T., & Brown, D. A. (2008). Management control systems as a package: opportunities, challenges and research directions. Management Accounting Research, 19(2), 287–300.CrossRefGoogle Scholar
  25. 25.
    Merchant, K. A., & Van der Stede, W. A. (2007). Management control systems: Performance measurement, evaluation and incentives. Harlow: Pearson Education.Google Scholar
  26. 26.
    Müller, W. (1974). Die Koordination von Informationsbedarf und Informationsbeschaffung als zentrale Aufgabe des Controlling. Zeitschrift für betriebswirtschaftliche Forschung, 26(10), 683–693.Google Scholar
  27. 27.
    Østergren, K., & Stensaker, I. (2008). Management control without budgets: A field study of ‘beyond budgeting’ in practice. Journal of European Accounting Review, 20(1), 149–181.CrossRefGoogle Scholar
  28. 28.
    Peffers, K., Tuunanen, T., Rothenberger, M. A., & Chatterjee, S. (2007). A design science research methodology for information systems research. Journal of Management Information Systems, 24(3), 45–77.CrossRefGoogle Scholar
  29. 29.
    Salman, T. (2008). Variance analysis as a tool for management control. Ilorin: Published Case Study University of Ilorin.Google Scholar
  30. 30.
    Schäffer, U., & Weber, J. (2016). Controlling 4.0. Controlling & Management Review, 3.Google Scholar
  31. 31.
    Simon, H. A. (1996). The science of artificial. Cambridge: The MIT Press.Google Scholar
  32. 32.
    Simons, R. (1994). Levers of control: How managers use innovative control systems to drive strategic renewal. Boston: Harvard Business Press.Google Scholar
  33. 33.
    Sierpińska, M., & Niedbała, B. (2003). Operational controlling in an enterprise. Warsaw: PWN.Google Scholar
  34. 34.
    Smith, M. (1998). New management accounting tools. Warsaw: Foundation for Accountancy Development in Poland.Google Scholar
  35. 35.
    Taylor, H. M. (1968). The economic design of cumulative sum control charts for variables. Technometrics, X, 479–488.CrossRefGoogle Scholar
  36. 36.
    Uwe, M., et al. (2012). Controlling process model. A guideline for describing and designing controlling processes. Haufe Verlag: Horváth & Partner, International Group of Controlling.Google Scholar
  37. 37.
    Walls, J. G., Widmeyer, G. R., & El Sawy, O. A. (1992). Building an information system design theory for vigilant EIS. Information Systems Research, 3(1), 36–59.CrossRefGoogle Scholar
  38. 38.
    Weber, C. (1963). The mathematics of variance analysis. The Accounting Review, 38(3), 534–539.Google Scholar
  39. 39.
    Weber, J., & Schäffer, U. (1999). Controlling durch die Nutzung des fruchtbaren Spannungsverhältnisses von Reflexion und Intuitionen. Zeitschrift für Planung, 10(2), 205–224.Google Scholar
  40. 40.
    Zannetos, Z. S. (1963). On the mathematics of variance analysis. The Accounting Review, 38(3), 528–533.Google Scholar

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Faculty of ManagementWroclaw University of Economics and BusinessWroclawPoland

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